sahilbhange/No-Show-Patients-Analysis

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R
Created over 6 years ago
Updated over 6 years ago

sahilbhange/No-Show-Patients-Analysis

sahilbhange

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Model to predict patients who will likely to miss the booked appointment using logistic regression and random forest machine learning techniques

hive-sql-slowly-changing-dimension

Slowly Changing Dimension type 2 using Hive query language using exclusive join technique with ORC Hive tables, partitioned and clustered hive table performance comparison

Facebook-Data-Extraction

#DataPipeLine #ETL - Created is a Facebook data extraction utility to extract the publicly available data on Facebook. Used Facebook Graph API and Python to extract the data and loaded the data into the CSV files for further analysis.

spark-slowly-changing-dimension

Spark implementation of Slowly Changing Dimension type 2

YouTube-comments-Spam-Detector

YouTube Spam comments classifier using Naive Bayes and SVM

Jupyter Notebook

Spark-Practice-Repository

Apache Spark practice (Core API, Data Frames and Spark SQL) using Python

Defensive-Forecasting

Defensive Forecasting is an online forecasting technique for Binary Labels

Machine-Learning-Assignments-1

Machine learning course assignments

Kiva-Loan-Data-Warehouse

#Pyspark#HDFS#Spark#DataAnalysis - Kiva loan data mart will be used to transform and analyse Kiva loan data and to help understand lenders targeted loan community

Stayzilla-Operation-Failure-Analysis

The project is to analyze the operation failure of the Stayzilla.com (an Airbnb like startup in India)

Exploratory-and-Descriptive-Analysis-on-Loan-Dataset

The project is to conduct a set of exploratory analysis and performing various machine learning techniques to predict loan borrower’s default rate with that we have tried various data visualization techniques to show data distribution

Intro-to-Data-Science-Assignment

Introduction to Data Science course Assignments

Jupyter Notebook

Text-Language-Detector

Language Detector program using Google Translator API